Healthcare Research Scientist

Joint Commission seeks a Healthcare Research Scientist with deep expertise in causal inference and healthcare data analytics to advance our enterprise-wide research and improvement agenda. The ideal candidate will apply rigorous statistical methods to observational data to generate actionable insights that support health system improvement and public accountability.

Responsibilities:

• Design and lead applied research studies that estimate associations and causal effects from non-randomized healthcare data, contribute to Joint Commissions priorities in quality measurement, accreditation and certification evaluation, and system-level learning
• Develop and implement advanced analytic methods, including but not limited to instrumental variables, difference-in-differences, regression discontinuities marginal structural models, propensity score-based techniques, synthetic controls, and machine learning
• Work with JC’s diverse data resources, including accreditation survey findings and patient level data (PLD) from participating health systems, as well as external sources such as Medicare and Medicaid claims
• Build relationships and consensus across operational, clinical, and business functions to support the development, execution, and dissemination of research that advances Joint Commission priorities
• Translate research findings into accessible, policy-relevant insights for diverse audiences including business leaders, regulators, hospitals, and the public
• Ensure that public-facing research outputs and communication reflect the strategic and reputational interests of the Joint Commission
• Publish findings in peer-reviewed journals and contribute to internal strategic products, performance improvement resources, and external stakeholder briefings
• Develop non-technical memos, briefs, and other materials to inform internal leadership decisions and support external stakeholder engagement
• Collaborate with cross-functional teams within the Joint Commission, the National Quality Forum (NQF), and external partners to inform accreditation standards, quality improvement engagement, benchmarking, and outcomes-driven certification
• Provide direction to and oversight of data analysts and research assistants assigned to support project work, ensuring high-quality and timely execution of analytic tasks
• Contribute to a culture of rigor, transparency, and equity in research planning, execution, and dissemination

Requirements:

• PhD or equivalent in economics, health services research, epidemiology, biostatistics, public policy, or a related quantitative field
• 2 years post-doctoral experience applying causal inference methods to real-world healthcare data, especially administrative claims
• Peer-reviewed publication record in areas such as healthcare economics, outcomes, delivery, policy, quality, or safety
• Demonstrated success communicating analytic findings and familiarizing non-researchers with research methods
• Ability to independently code, execute, and troubleshoot statistical analyses in R, Stata, or Python
• Commitment to Joint Commission's mission to continuously improve healthcare for the public, in collaboration with key stakeholders
• Demonstrated track record of designing and overseeing analytic projects from concept through execution, including managing scope, timelines, and outputs in research or applied settings
• Experience with Joint Commission's quality process and outcome measures in prior work
• Experience conducting research with or within an organization where outputs must align with institutional mission and communications strategy (e.g., ASPE, CMS, AHRQ, VA, state agencies, consulting companies, etc.)
• Familiarity with predictive modeling or statistical learning methods is welcome, especially when integrated with causal inference approaches and natural language processing (NLP)
• Familiarity with regulatory, payer, or policy contexts (e.g., CMS Conditions of Participation, state Medicaid programs, or alternative payment models)
• Experience working in cross-functional teams, including collaborating across roles and managing project-based responsibilities without direct supervisory authority